What would we do without Linear Algebra, Part II: Diving Deeper, Singular Value Decomposition
About This Video
Prerequisite: We will assume that you are familiar with the vector and matrix algebra.
This the second workshop devoted to linear algebra, which forms the foundation of many algorithms in data science. In part I of the series we introduced vector and matrix algebra, and briefly looked at the intriguing and ever so useful Singular Value Decomposition (SVD). In this workshop, we will take a deeper into the SVD. We will explain how it is derived, how it can be computed, and also how it is used.
This workshop is taught by Professor Margot Gerritsen and Stanford ICME PhD student, Laura Lyman.
In This Video
Co-Founder, WiDS Worldwide
Margot Gerritsen is a Professor at Stanford University and co-founder and co-director of WiDS Worldwide. Her expertise is in computational mathematics. She is particularly fond of computational fluid dynamics and linear algebra. Margot received her MS from TU Delft and her PhD from Stanford. Prior to her position at Stanford, she spent five years as a faculty member at the University of Auckland, New Zealand. Margot was born and raised in the Netherlands and left the flat lands in 1990 in search of hillier and sunnier places. She still has her Dutch accent. She currently lives in the Oregon mountains with her partner Paul.